A mechanism informed neural network for predicting machining deformation of annular parts |
| |
| Affiliation: | 1. College of Mechanical & Electrical Engineering/ National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China;1. Electrical & Computer Engineering Department, Tarbiat Modares University, Tehran, Iran;2. Faculty of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran;1. Department of Engineering, University of Cambridge, United Kingdom;2. Department of Technology, Illinois State University, United States;3. Department of Neuroscience, Physiology, and Pharmacology, University College London, United Kingdom;4. Laing O’Rourke Professor, Department of Engineering, University of Cambridge, United Kingdom;1. Graduate School of Culture Technology, KAIST, Daejeon, The Republic of Korea;2. Department of Convergence IT Engineering, POSTECH, Pohang, The Republic of Korea;1. College of Safety Science & Engineering, Liaoning Technical University, Huludao 125105, China;2. Key Laboratory of Mine Thermo-motive Disaster & Prevention, Ministry of Education, Huludao 125105, China;3. School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China;1. School of Civil Aviation, Northwestern Polytechnical University, 710072 Xi’an, China;2. COMAC Flight Test Center, 201207 Shanghai, China |
| |
| Abstract: | Controlling machining deformation of annular parts is crucial for ensuring the performance of high value products and equipment. For example, during manufacturing of critical parts in aircrafts and spacecrafts, accurate prediction of machining deformation is the basis for guiding the formulation of deformation control strategies. However, due to the complexity of the machining deformation of annular parts, existing methods still have limitations in accurate prediction. To this end, this paper proposes a mechanism informed neural network (MINN) to predict machining deformation of annular parts. MINN is realized by establishing the dual sub-networks structure and using enhanced loss functions with the consideration of the deformation mechanism model characteristics of annular parts. The deformation was decomposed into the axisymmetric portion and the non-axisymmetric portion according to the deformation superposition principle, and modeled separately based on the thin-shell theory and Fourier series. Experiment results showed that the proposed method could predict the machining deformation of annular parts more accurately and stably with a small amount of training data, compared with previous methods. |
| |
| Keywords: | Annular part Machining deformation prediction Data-mechanism fusion Thin-shell theory Fourier series |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
